The proposed framework was developed using Python 3.7.4 with TensorFlow 2.2.0 and Keras APIs on a machine havingIntel Core i9-10940X@3.30 GHz CPU, 128 GB RAM and an NVIDIA Quadro RTX 6000 with cuDNN v7.5, and a CUDA Toolkit 10.1.243. Some utility functions are also implemented using MATLAB R2021a. Apart from this, the training on each dataset was conducted for a maximum of 50 epochs using ADADELTA [1]} as an optimizer (with the default learning and decay rate configurations) and a batch size of 4. Moreover, 10% of the training samples from each dataset were used for the validation (after each epoch).
For the loss function, we used the focal loss [2]} expressed below:
\(l_f = -\frac{1}{b_s}\sum \limits _{i=0}^{b_s-1}\sum \limits _{j=0}^{c-1} \alpha (1-p(l_{i,j}))^\gamma t_{i,j}\log (p(l_{i,j}))\) 
